scholarly journals Evaluation of Seasonal, Drought, and Wet Condition Effects on Performance of Satellite-Based Precipitation Data over Different Climatic Conditions in Iran

2021 ◽  
Vol 14 (1) ◽  
pp. 76
Author(s):  
Salman Qureshi ◽  
Javad Koohpayma ◽  
Mohammad Karimi Firozjaei ◽  
Ata Abdollahi Kakroodi
Keyword(s):  
Satellite Data ◽  
Precipitation Amount ◽  
Standardized Precipitation Index ◽  
Tropical Rainfall Measuring Mission ◽  
Climatic Conditions ◽  
Seasonal Effects ◽  
Monthly Precipitation ◽  
Climate Conditions ◽  
The Standardized Precipitation Index ◽  
Semi Arid

The Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) are the most important and widely used data sources in several applications—e.g., forecasting drought and flood, and managing water resources—especially in the areas with sparse or no other robust sources. This study explored the accuracy and precision of satellite data products over a span of 18 years (2000–2017) using synoptic ground station data for three regions in Iran with different climates, namely (a) humid and high rainfall, (b) semi-arid, and (c) arid. The results show that the monthly precipitation products of GPM and TRMM overestimate the rainfall. On average, they overestimated the precipitation amount by 11% in humid, by 50% in semi-arid, and by 43% in arid climate conditions compared to the ground-based data. This study also evaluated the satellite data accuracy in drought and wet conditions based on the standardized precipitation index (SPI) and different seasons. The results showed that the accuracy of satellite data varies significantly under drought, wet, and normal conditions and different timescales, being lowest under drought conditions, especially in arid regions. The highest accuracy was obtained on the 12-month timescale and the lowest on the 3-month timescale. Although the accuracy of the data is dependent on the season, the seasonal effects depend on climatic conditions.

Impact of basin-wide dry climate conditions and non-climatic drivers: an isolation approach

2016 ◽  
Vol 7 (3) ◽  
pp. 498-513 ◽  
Author(s):  
Furat A. M. Al-Faraj ◽  
Miklas Scholz
Keyword(s):  
Water Resources ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
River Regulation ◽  
Relative Effect ◽  
Climatic Conditions ◽  
Climate Conditions ◽  
Decision Making Processes ◽  
Diyala River ◽  
The Standardized Precipitation Index

The sustainable management of water resources subjected to the joined influence of transboundary basin-wide dry climatic conditions and intensive man-made river regulations in an upper riparian state on the stream flow regime of a downstream country is a serious challenge. This is particularly the case for arid and semi-arid regions where water resources are limited. The Diyala river basin, shared between Iraq and Iran, was used as an example. The study aims to develop a generic approach to isolate the relative effect of upstream man-made interventions from the mutual impacts of basin-wide dry climate environments and upstream human-induced pressures. The proposed method supports water managers in unbiased, timely and spatially relevant decision-making processes. The streamflow drought index and the monthly-based truncation level were utilized to characterize hydrological droughts, while the standardized precipitation index was used for meteorological drought interpretations. Findings revealed that the upstream river regulation schemes noticeably led to a decline in water availability of the downstream country. The relative impact ranged from a minimum value of 5% in February to the highest value of 54% in July. The average proportional impacts between April and October and between November and March were about 46% and 17%, respectively.

scholarly journals Evaluation of Seasonal and Drought Conditions Effect on Satellite-based Precipitation Data Accuracy over Different Climatic Conditions

2020 ◽  
Author(s):  
Salman Qureshi ◽  
Javad Koohpayma ◽  
Mohammad Karimi Firozjaei ◽  
Ata Abdollahi Kakroodi
Keyword(s):  
Satellite Data ◽  
Time Scale ◽  
Climatic Conditions ◽  
Data Accuracy ◽  
Arid Climate ◽  
Correction Coefficient ◽  
Monthly Precipitation ◽  
Tropical Regions ◽  
Drought Conditions ◽  
Semi Arid

Abstract: The Tropical Rainfall Measuring Mission (TRMM) and then Global Precipitation Mission (GPM) are the most important and widely used data sources in the forecasting of drought, flood, and water resources management. However, since this sensor’s data is primarily used for tropical regions, it is necessary to evaluate the accuracy for optimal use of the data across varying climatic and physiographic conditions. In this study, the accuracy of the satellite data for a span of 17 years (2000-2017) for three climatic zones has been explored using synoptic ground station data. The climates include a) arid and low rainfall, b) semi-arid and low rainfall, and c) humid and high rainfall. We evaluated satellite data accuracy in drought and wet conditions based on the Standard Precipitation Index (S.P.I.) and different seasons. For available ground control stations, 13 stations were used in the humid, seven stations in a semi-arid climate, and 12 stations in the dry climate. The results show that the monthly precipitation product of GPM (IMERG product) and TRMM (TMPA/3B43 product) overestimate the rainfall. In the arid climate, the precipitation is estimated 43%, in the semi-arid environment 50%, and in the humid weather 11% more than the ground-based data on average. Therefore, to use satellite data in different climates, it is necessary to make corrections to obtain precise results. Based on 32 ground stations, the correction coefficient has a positive relationship with average precipitation and altitude and an inverse relationship with the latitude. Further in-depth investigations showed that the accuracy of satellite data in wet conditions is higher than the accuracy of normal circumstances, and the accuracy of normal conditions is more accurate than drought conditions. Besides, the accuracy of satellite data in wet or dry conditions increases with increasing time scales. The highest accuracy was obtained for a 12-month time scale and the lowest accuracy for the 3-month time scale of drought conditions in the arid climate.

DROUGHT OCCURRENCE UNDER LITHUANIAN CLIMATIC CONDITIONS

2015 ◽  
Author(s):  
Laima TAPARAUSKIENĖ ◽  
Veronika LUKŠEVIČIŪTĖ
Keyword(s):  
Meteorological Data ◽  
Standardized Precipitation Index ◽  
Vegetation Period ◽  
Climatic Conditions ◽  
Precipitation Record ◽  
Calculation Step ◽  
Drought Conditions ◽  
The Standardized Precipitation Index ◽  
June July

This study provides the analysis of drought conditions of vegetation period in 1982-2014 year in two Lithuanian regions: Kaunas and Telšiai. To identify drought conditions the Standardized Precipitation Index (SPI) was applied. SPI was calculated using the long-term precipitation record of 1982–2014 with in-situ meteorological data. Calculation step of SPI was taken 1 month considering only vegetation period (May, June, July, August, September). The purpose of investigation was to evaluate the humidity/aridity of vegetation period and find out the probability of droughts occurrence under Lithuanian climatic conditions. It was found out that according SPI results droughts occurred in 14.5 % of all months in Kaunas region and in 15.8 % in Telšiai region. Wet periods in Kaunas region occurred in 15.8 %, and in Telšiai region occurrence of wet periods was – 18.8 % from all evaluated months. According SPI evaluation near normal were 69.7 % of total months during period of investigation in Kaunas and respectively – 65.5 % in Telšiai. The probability for extremely dry period under Lithuania climatic conditions are pretty low – 3.0 % in middle Lithuania and 2.4 % in western part of Lithuania.

scholarly journals Seasonal forecasts of droughts in African basins using the Standardized Precipitation Index

2013 ◽  
Vol 17 (6) ◽  
pp. 2359-2373 ◽  
Author(s):  
E. Dutra ◽  
F. Di Giuseppe ◽  
F. Wetterhall ◽  
F. Pappenberger
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Seasonal Forecasting ◽  
Monthly Precipitation ◽  
Seasonal Forecasts ◽  
Blue Nile ◽  
Forecasting System ◽  
The Standardized Precipitation Index

Abstract. Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas, which often have a very low resilience and limited capabilities to mitigate drought impacts. This paper assesses the predictive capabilities of an integrated drought monitoring and seasonal forecasting system (up to 5 months lead time) based on the Standardized Precipitation Index (SPI). The system is constructed by extending near-real-time monthly precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System–Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with monthly forecasted fields as provided by the ECMWF seasonal forecasting system. The forecasts were then evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. The generally low number of rain gauges and their decrease in the recent years limits the verification and monitoring of droughts in the different basins, reinforcing the need for a strong investment on climate monitoring. All the datasets show similar spatial and temporal patterns in southern and north-western Africa, while there is a low correlation in the equatorial area, which makes it difficult to define ground truth and choose an adequate product for monitoring. The seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depend strongly on the SPI timescale, and longer timescales have more skill. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near-real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor-quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in 2 to 4 months lead time.

scholarly journals Application of TRMM Precipitation Data to Evaluate Drought and Its Effects on Water Resources Instability

2019 ◽  
Vol 9 (24) ◽  
pp. 5377
Author(s):  
Ata Amini ◽  
Abdolnabi Abdeh Kolahchi ◽  
Nadhir Al-Ansari ◽  
Mehdi Karami Moghadam ◽  
Thamer Mohammad
Keyword(s):  
Water Resources ◽  
Standardized Precipitation Index ◽  
Remote Sensing Data ◽  
Tropical Rainfall Measuring Mission ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Discharge Data ◽  
Satellite Precipitation ◽  
Discharge Rates ◽  
Trmm Precipitation

The present research was carried out to study drought and its effects upon water resources using remote sensing data. To this end, the tropical rainfall measuring mission (TRMM) satellite precipitation, the synoptic stations, and fountain discharge data were employed. For monitoring of drought in the study area, in Kermanshah province, Iran, the monthly precipitation data of the synoptic stations along with TRMM satellite precipitation datasets were collected and processed in the geographic information system (GIS) environment. Statistical indicators were applied to evaluate the accuracy of TRMM precipitation against the meteorological stations’ data. Standardized precipitation index, SPI, and normalized fountain discharge were used in the monitoring of drought conditions, and fountains discharge, respectively. The fountains were selected so that in addition to enjoying the most discharge rates, they spread along the study area. The evaluation of precipitation data showed that the TRMM precipitation data were of high accuracy. Studies in temporal scale are indicative of the strike of drought in this region to the effect that for most months of the year, frequency and duration in dry periods are much more than in wet periods. As for seasonal scales, apart from winter, the frequency and duration of drought in spring and autumn have been longer than in wet years. Moreover, the duration of these periods was different. A comparison between the results of changes in fountain discharges and drought index in the region has verified that the drought has caused a remarkable decline in the fountain discharges.

scholarly journals Hydrological differentiation and spatial distribution of high altitude wetlands in a semi-arid Andean region derived from satellite data

2011 ◽  
Vol 8 (1) ◽  
pp. 1287-1327 ◽  
Author(s):  
M. Otto ◽  
D. Scherer ◽  
J. Richters
Keyword(s):  
High Altitude ◽  
Satellite Data ◽  
Regional Scale ◽  
Austral Summer ◽  
Monthly Precipitation ◽  
Austral Winter ◽  
Andean Region ◽  
Study Region ◽  
The Andes ◽  
Semi Arid

Abstract. High Altitude Wetlands of the Andes (HAWA) are unique types of wetlands within the semi-arid high Andean region. Knowledge about HAWA has been derived mainly from studies at single sites within different parts of the Andes at only small time scales. On the one hand HAWA depend on water provided by glacier streams, snow melt or precipitation. On the other hand, they are suspected to influence hydrology through water retention and vegetation growth altering stream flow velocity. We derived HAWA land cover from satellite data at regional scale and analysed changes in connection with precipitation over the last decade. Perennial and temporal HAWA subtypes can be distinguished by seasonal changes of photosynthetically active vegetation (PAV) indicating the perennial or temporal availability of water during the year. HAWA have been delineated within a region of 11 000 km2 situated in the Northwest of Lake Titicaca. The multi temporal classification method used Normalized Differenced Vegetation Index (NDVI) and Normalized Differenced Infrared Index (NDII) data derived from two Landsat ETM+ scenes at the end of austral winter (September 2000) and at the end of austral summer (May 2001). The mapping result indicates an unexpected high abundance of HAWA covering about 800 km2 of the study region (6%). Annual HAWA mapping was computed using NDVI 16-day composites of Moderate Resolution Imaging Spectroradiometer (MODIS). Analyses on the reletation between HAWA and precipitation was based on monthly precipitation data of the Tropical Rain Measurement Mission (TRMM 3B43) and MODIS Eight Day Maximum Snow Extent data (MOD10A2) from 2000 to 2010. We found HAWA subtype specific dependencies to precipitation conditions. Strong relation exists between perennial HAWA and snow fall (r2: 0.82) in dry austral winter months (June to August) and between temporal HAWA and precipitation (r2: 0.75) during austral summer (March to May). Annual spatial patterns of perennial HAWA indicated spatial alteration of water supply for PAV up to several hundred metres at a single HAWA site.

scholarly journals Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran

2021 ◽  
Author(s):  
Morteza Lotfirad ◽  
Hassan Esmaeili-Gisavandani ◽  
Arash Adib
Keyword(s):  
Random Forest ◽  
Correlation Coefficient ◽  
Potential Evapotranspiration ◽  
Standardized Precipitation Index ◽  
Monthly Precipitation ◽  
Model Combination ◽  
The North ◽  
Drought Prediction ◽  
Lag Times ◽  
The Standardized Precipitation Index

Abstract The aim of this study is to select the best model (combination of different lag times) for predicting the standardized precipitation index (SPI) and the standardized precipitation and evapotranspiration index (SPEI) in next time. Monthly precipitation and temperature data from 1960 to 2019 were used. In temperate climates, such as the north of Iran, the correlation coefficient of SPI and SPEI was 0.94, 0.95, and 0.81 at the time scales of 3, 12, and 48 months, respectively. Besides, this correlation coefficient was 0.47, 0.35, and 0.44 in arid and hot climates, such as the southwest of Iran because potential evapotranspiration (PET) depends on temperature more than rainfall. Drought was predicted using the random forest (RF) model and applying 1–12 months lag times for next time. By increasing of time scale, the prediction accuracy of SPI and SPEI will improve. The ability of SPEI is more than SPI for drought prediction, because the overall accuracy (OA) of prediction will increase, and the errors (i.e., overestimate (OE) and underestimate (UE)) will reduce. It is recommended for future studies (1) using wavelet analysis for improving accuracy of predictions and (2) using the Penman–Monteith method if ground-based data are available.

scholarly journals Drought Monitoring Utility using Satellite-Based Precipitation Products over the Xiang River Basin in China

2019 ◽  
Vol 11 (12) ◽  
pp. 1483 ◽  
Author(s):  
Qian Zhu ◽  
Yulin Luo ◽  
Dongyang Zhou ◽  
Yue-Ping Xu ◽  
Guoqing Wang ◽  
...  
Keyword(s):  
River Basin ◽  
Drought Index ◽  
Standardized Precipitation Index ◽  
Tropical Rainfall Measuring Mission ◽  
Drought Monitoring ◽  
In Situ Observations ◽  
Remote Sensing Techniques ◽  
Xiang River ◽  
The Standardized Precipitation Index

Drought is a natural hazard disaster that can deeply affect environments, economies, and societies around the world. Therefore, accurate monitoring of patterns in drought is important. Precipitation is the key variable to define the drought index. However, the spare and uneven distribution of rain gauges limit the access of long-term and reliable in situ observations. Remote sensing techniques enrich the precipitation data at different temporal–spatial resolutions. In this study, the climate prediction center morphing (CMORPH) technique (CMORPH-CRT), the tropical rainfall measuring mission (TRMM) multi-satellite precipitation analysis (TRMM 3B42V7), and the integrated multi-satellite retrievals for global precipitation measurement (IMERG V05) were evaluated and compared with in situ observations for the drought monitoring in the Xiang River Basin, a humid region in China. A widely-used drought index, the standardized precipitation index (SPI), was chosen to evaluate the drought monitoring utility. The atmospheric water deficit (AWD) was used for comparison of the drought estimation with SPI. The results were as follows: (1) IMERG V05 precipitation products showed the highest accuracy against grid-based precipitation, followed by CMORPH-CRT, which performed better than TRMM 3B42V7; (2) IMERG V05 showed the best performance in SPI-1 (one-month SPI) estimations compared with CMORPH-CRT and TRMM 3B42V7; (3) SPI-1 was more suitable for drought monitoring than AWD in the Xiang River Basin, because its high R-values and low root mean square error (RMSE) compared with the corresponding index based on in situ observations; (4) drought conditions in 2015 were apparently more severe than that in 2016 and 2017, with the driest area mainly distributed in the southwest part of the Xiang River Basin.

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